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Wang X, Gao X, Fan X, Huai Z, Zhang G, Yao M, Wang T, Huang X, Lai L. WUREN: Whole-modal union representation for epitope prediction. Comput Struct Biotechnol J 2024; 23:2122-2131. [PMID: 38817963 PMCID: PMC11137340 DOI: 10.1016/j.csbj.2024.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.
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Affiliation(s)
| | | | - Xuezhe Fan
- XtalPi Innovation Center, Beijing, China
| | - Zhe Huai
- XtalPi Innovation Center, Beijing, China
| | | | | | | | | | - Lipeng Lai
- XtalPi Innovation Center, Beijing, China
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2
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Chu L, Ruffolo JA, Harmalkar A, Gray JJ. Flexible protein-protein docking with a multitrack iterative transformer. Protein Sci 2024; 33:e4862. [PMID: 38148272 PMCID: PMC10804679 DOI: 10.1002/pro.4862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/17/2023] [Accepted: 12/06/2023] [Indexed: 12/28/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee‐Shin Chu
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey A. Ruffolo
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
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3
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Liu C, Denzler LM, Hood OE, Martin AC. Do antibody CDR loops change conformation upon binding? MAbs 2024; 16:2322533. [PMID: 38477253 PMCID: PMC10939163 DOI: 10.1080/19420862.2024.2322533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Antibodies have increasingly been developed as drugs with over 100 now licensed in the US or EU. During development, it is often necessary to increase or reduce the affinity of an antibody and rational attempts to do so rely on having a structure of the antibody-antigen complex often obtained by modeling. The antigen-binding site consists primarily of six loops known as complementarity-determining regions (CDRs), and an open question has been whether these loops change their conformation when they bind to an antigen. Existing surveys of antibody-antigen complex structures have only examined CDR conformational change in case studies or small-scale surveys. With an increasing number of antibodies where both free and complexed structures have been deposited in the Protein Data Bank, a large-scale survey of CDR conformational change during binding is now possible. To this end, we built a dataset, AbAgDb, that currently includes 177 antibodies with high-quality CDRs, each of which has at least one bound and one unbound structure. We analyzed the conformational change of the Cα backbone of each CDR upon binding and found that, in most cases, the CDRs (other than CDR-H3) show minimal movement, while 70.6% and 87% of CDR-H3s showed global Cα RMSD ≤ 1.0Å and ≤ 2.0Å, respectively. We also compared bound CDR conformations with the conformational space of unbound CDRs and found most of the bound conformations are included in the unbound conformational space. In future, our results will contribute to developing insights into antibodies and new methods for modeling and docking.
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Affiliation(s)
- Chu’nan Liu
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Lilian M. Denzler
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Oliver E.C. Hood
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Andrew C.R. Martin
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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4
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Nervig CS, Gustat JR, Owen SC. Split-Protein Therapeutic Platforms: Identifying Binder Pairs. Methods Mol Biol 2024; 2720:75-84. [PMID: 37775658 DOI: 10.1007/978-1-0716-3469-1_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2023]
Abstract
Therapeutic proteins, including enzymes, interferons, interleukins, and growth factors, are emerging as important modalities to treat many diseases that elude management by small molecule drugs. One challenge of protein treatment is the propensity for off-target or systemic activity. A promising approach to overcome such toxicity is to create conditionally active constructs by splitting the therapeutic protein into two, or more, inactive fragments and by fusing these fragments to binders (e.g., antibodies) that target distinct epitopes on a cell surface. When these antibodies bind to their respective targets, the protein fragments are brought into proximity and then reconstitute into the active form of the therapeutic protein. In this chapter, we describe approaches to determine antibody pairs that enable the reconstitution of the active protein. General computational and empirical methods are provided to facilitate the identification of pairs starting only from protein sequence data.
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Affiliation(s)
- Christine S Nervig
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, UT, USA
| | - James R Gustat
- Department of Molecular Pharmaceutics, University of Utah, Salt Lake City, UT, USA
| | - Shawn C Owen
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, UT, USA.
- Department of Molecular Pharmaceutics, University of Utah, Salt Lake City, UT, USA.
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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5
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Yin R, Pierce BG. Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy. Protein Sci 2024; 33:e4865. [PMID: 38073135 PMCID: PMC10751731 DOI: 10.1002/pro.4865] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 12/26/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
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6
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Zhang G, Su Z, Zhang T, Wu Y. Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570397. [PMID: 38106177 PMCID: PMC10723427 DOI: 10.1101/2023.12.06.570397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.
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Affiliation(s)
- Grace Zhang
- Staples High School, 70 North Avenue, Westport, CT 06880
| | - Zhaoqian Su
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN, 37212
| | - Tom Zhang
- California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461
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7
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Olechnovič K, Valančauskas L, Dapkūnas J, Venclovas Č. Prediction of protein assemblies by structure sampling followed by interface-focused scoring. Proteins 2023; 91:1724-1733. [PMID: 37578163 DOI: 10.1002/prot.26569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/12/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Abstract
Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lukas Valančauskas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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8
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Lensink MF, Brysbaert G, Raouraoua N, Bates PA, Giulini M, Honorato RV, van Noort C, Teixeira JMC, Bonvin AMJJ, Kong R, Shi H, Lu X, Chang S, Liu J, Guo Z, Chen X, Morehead A, Roy RS, Wu T, Giri N, Quadir F, Chen C, Cheng J, Del Carpio CA, Ichiishi E, Rodriguez‐Lumbreras LA, Fernandez‐Recio J, Harmalkar A, Chu L, Canner S, Smanta R, Gray JJ, Li H, Lin P, He J, Tao H, Huang S, Roel‐Touris J, Jimenez‐Garcia B, Christoffer CW, Jain AJ, Kagaya Y, Kannan H, Nakamura T, Terashi G, Verburgt JC, Zhang Y, Zhang Z, Fujuta H, Sekijima M, Kihara D, Khan O, Kotelnikov S, Ghani U, Padhorny D, Beglov D, Vajda S, Kozakov D, Negi SS, Ricciardelli T, Barradas‐Bautista D, Cao Z, Chawla M, Cavallo L, Oliva R, Yin R, Cheung M, Guest JD, Lee J, Pierce BG, Shor B, Cohen T, Halfon M, Schneidman‐Duhovny D, Zhu S, Yin R, Sun Y, Shen Y, Maszota‐Zieleniak M, Bojarski KK, Lubecka EA, Marcisz M, Danielsson A, Dziadek L, Gaardlos M, Gieldon A, Liwo A, Samsonov SA, Slusarz R, Zieba K, Sieradzan AK, Czaplewski C, Kobayashi S, Miyakawa Y, Kiyota Y, Takeda‐Shitaka M, Olechnovic K, Valancauskas L, Dapkunas J, Venclovas C, Wallner B, Yang L, Hou C, He X, Guo S, Jiang S, Ma X, Duan R, Qui L, Xu X, Zou X, Velankar S, Wodak SJ. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment. Proteins 2023; 91:1658-1683. [PMID: 37905971 PMCID: PMC10841881 DOI: 10.1002/prot.26609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 11/02/2023]
Abstract
We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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Affiliation(s)
- Marc F. Lensink
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Guillaume Brysbaert
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Nessim Raouraoua
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Paul A. Bates
- Biomolecular Modeling LaboratoryThe Francis Crick InstituteLondonUK
| | - Marco Giulini
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Rodrigo V. Honorato
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Charlotte van Noort
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Joao M. C. Teixeira
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Jian Liu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Zhiye Guo
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Xiao Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Alex Morehead
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Raj S. Roy
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Tianqi Wu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Nabin Giri
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Farhan Quadir
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Chen Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Jianlin Cheng
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | | | - Eichiro Ichiishi
- International University of Health and Welfare (IUHV Hospital)Nasushiobara‐CityJapan
| | - Luis A. Rodriguez‐Lumbreras
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Juan Fernandez‐Recio
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Ameya Harmalkar
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Lee‐Shin Chu
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Sam Canner
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Rituparna Smanta
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Hao Li
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Peicong Lin
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jiahua He
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Huanyu Tao
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Sheng‐You Huang
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jorge Roel‐Touris
- Protein Design and Modeling Lab, Dept. of Structural BiologyMolecular Biology Institute of Barcelona (IBMB‐CSIC)BarcelonaSpain
| | | | | | - Anika J. Jain
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuki Kagaya
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Harini Kannan
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | - Tsukasa Nakamura
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Genki Terashi
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Jacob C. Verburgt
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuanyuan Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Zicong Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Hayato Fujuta
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | | | - Daisuke Kihara
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | | | | | | | | | | | | | | | - Surendra S. Negi
- Sealy Center for Structural Biology and Molecular BiophysicsUniversity of Texas Medical BranchGalvestonTexasUSA
| | | | | | - Zhen Cao
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Mohit Chawla
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
- Department of Chemistry and BiologyUniversity of SalernoFiscianoItaly
| | | | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Chemistry and BiochemistryUniversity of MarylandCollege ParkMarylandUSA
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Jessica Lee
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Ben Shor
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Tomer Cohen
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Matan Halfon
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | | | - Shaowen Zhu
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Rujie Yin
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yuanfei Sun
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yang Shen
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTexasUSA
- Institute of Biosciences and Technology and Department of Translational Medical SciencesTexas A&M UniversityHoustonTexasUSA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yuta Miyakawa
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | - Yasuomi Kiyota
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | | | - Kliment Olechnovic
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Lukas Valancauskas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Justas Dapkunas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Ceslovas Venclovas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Bjorn Wallner
- Bioinformatics Division, Department of Physics, Chemistry, and BiologyLinkoping UniversityLinköpingSweden
| | - Lin Yang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- School of Aerospace, Mechanical and Mechatronic EngineeringThe University of SydneyNew South WalesAustralia
| | - Chengyu Hou
- School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
| | - Xiaodong He
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- Shenzhen STRONG Advanced Materials Research Institute Col, LtdShenzhenPeople's Republic of China
| | - Shuai Guo
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Shenda Jiang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Xiaoliang Ma
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Rui Duan
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Liming Qui
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xianjin Xu
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
- Dept. of Physics and AstronomyUniversity of MissouriColumbiaMissouriUSA
- Dept. of BiochemistryUniversity of MissouriColumbiaMissouriUSA
- Institute for Data Science and InformaticsUniversity of MissouriColumbiaMissouriUSA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonCambridgeUK
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9
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Sekar TV, Elghonaimy EA, Swancutt KL, Diegeler S, Gonzalez I, Hamilton C, Leung PQ, Meiler J, Martina CE, Whitney M, Aguilera TA. Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment. Nat Commun 2023; 14:7473. [PMID: 37978291 PMCID: PMC10656474 DOI: 10.1038/s41467-023-43038-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.
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Affiliation(s)
- Thillai V Sekar
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Microbiology, Pondicherry University, Kalapet, Puducherry, India
| | - Eslam A Elghonaimy
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Katy L Swancutt
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sebastian Diegeler
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Isaac Gonzalez
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Cassandra Hamilton
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter Q Leung
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
| | - Cristina E Martina
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michael Whitney
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Todd A Aguilera
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA.
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10
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Polonsky K, Pupko T, Freund NT. Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:1578-1588. [PMID: 37782047 DOI: 10.4049/jimmunol.2300150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023]
Abstract
Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.
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Affiliation(s)
- Ksenia Polonsky
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Tal Pupko
- Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Natalia T Freund
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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11
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Yuan Y, Chen Q, Mao J, Li G, Pan X. DG-Affinity: predicting antigen-antibody affinity with language models from sequences. BMC Bioinformatics 2023; 24:430. [PMID: 37957563 PMCID: PMC10644518 DOI: 10.1186/s12859-023-05562-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens. RESULTS In this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset. CONCLUSIONS Compared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .
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Affiliation(s)
- Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
| | | | - Jun Mao
- DigitalGene, Ltd, Shanghai, 200240, China
| | - Guipeng Li
- DigitalGene, Ltd, Shanghai, 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
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12
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Yang L, Guo S, Hou C, Jiang S, Shi L, Ma X, Zheng B, Fang Y, Ye L, He X. Low-Entropy Hydration Shells at the Spike RBD's Binding Site May Reveal the Contagiousness of SARS-CoV-2 Variants. Biomolecules 2023; 13:1628. [PMID: 38002310 PMCID: PMC10669249 DOI: 10.3390/biom13111628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/29/2023] [Accepted: 11/05/2023] [Indexed: 11/26/2023] Open
Abstract
The infectivity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is primarily determined by the binding affinity between the receptor-binding domain (RBD) of the spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor. Here, through screening off pseudo hydrophilic groups on protein surfaces, the distribution of low-entropy regions on hydration shells of the ACE2 receptor and the RBDs of multiple SARS-CoV-2 variants was demonstrated. Shape matching between the low-entropy hydration shells of multiple SARS-CoV-2 variants and the ACE2 receptor has been identified as a mechanism that drives hydrophobic attraction between the RBDs and the ACE2 receptor, which estimates the binding affinity. Low-entropy regions of the hydration shells, which play important roles in determining the binding of other viruses and their receptors, are demonstrated. The RBD-ACE2 binding is thus found to be guided by hydrophobic collapse between the shape-matched low-entropy regions of the hydration shells of the proteins. A measure of the low-entropy status of the hydration shells can be estimated by calculating genuine hydrophilic groups within the binding sites. An important indicator of the contagiousness of SARS-CoV-2 variants is the low-entropy level of its hydration shells at the spike protein binding site.
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Affiliation(s)
- Lin Yang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Shuai Guo
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
| | - Chengyu Hou
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China;
| | - Shenda Jiang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
| | - Liping Shi
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
| | - Xiaoliang Ma
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
| | - Bing Zheng
- Key Laboratory of Functional Inorganic Material Chemistry (Ministry of Education) and School of Chemistry and Materials Science, Heilongjiang University, Harbin 150001, China;
| | - Yi Fang
- Department of Mathematics, Nanchang University, Nanchang 330031, China;
| | - Lin Ye
- School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China;
| | - Xiaodong He
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150080, China; (S.G.); (S.J.); (L.S.); (X.M.)
- Shenzhen STRONG Advanced Materials Research Institute Co., Ltd., Shenzhen 518035, China
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13
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Gaudreault F, Corbeil CR, Sulea T. Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2. Sci Rep 2023; 13:15107. [PMID: 37704686 PMCID: PMC10499836 DOI: 10.1038/s41598-023-42090-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023] Open
Abstract
Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.
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Affiliation(s)
- Francis Gaudreault
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Christopher R Corbeil
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada.
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada.
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14
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Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LÁ, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin A, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson H, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Kihara D, Marchand A, Correia B, Zou X, Dey S, Dunbrack R, Levy E, Wodak S. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 2023; 23:e2200323. [PMID: 37365936 PMCID: PMC10937251 DOI: 10.1002/pmic.202200323] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Julia K. Varga
- Hebrew University of Jerusalem Institute for Medical Research Israel-Canada
| | | | | | | | | | | | | | - Jérôme Tubiana
- Tel Aviv University Blavatnik School of Computer Science
| | - Haim Wolfson
- Tel Aviv University Blavatnik School of Computer Science
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Institute for Data Science and Informatics, University of Missouri
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15
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Li J, Kang G, Wang J, Yuan H, Wu Y, Meng S, Wang P, Zhang M, Wang Y, Feng Y, Huang H, de Marco A. Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization. Int J Biol Macromol 2023; 247:125733. [PMID: 37423452 DOI: 10.1016/j.ijbiomac.2023.125733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.
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Affiliation(s)
- Jiaqi Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Guangbo Kang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jiewen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Haibin Yuan
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Yili Wu
- Zhejiang Provincial Clinical Research Center for Mental Disorders, School of Mental Health and the Affiliated Kangning Hospital, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Oujiang Laboratory, Wenzhou, Zhejiang 325035, China
| | - Shuxian Meng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Ping Wang
- New Technology R&D Department, Tianjin Modern Innovative TCM Technology Company Limited, Tianjin 300392, China
| | - Miao Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; China Resources Biopharmaceutical Company Limited, Beijing 100029, China
| | - Yuli Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Tianjin Pharmaceutical Da Ren Tang Group Corporation Limited, Traditional Chinese Pharmacy Research Institute, Tianjin Key Laboratory of Quality Control in Chinese Medicine, Tianjin 300457, China; State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, China
| | - Yuanhang Feng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - He Huang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia.
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16
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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17
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Zhao Y, He B, Xu F, Li C, Xu Z, Su X, He H, Huang Y, Rossjohn J, Song J, Yao J. DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis. SCIENCE ADVANCES 2023; 9:eabo5128. [PMID: 37556545 PMCID: PMC10411891 DOI: 10.1126/sciadv.abo5128] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
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Affiliation(s)
- Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bing He
- AI Lab, Tencent, Shenzhen, China
| | - Fan Xu
- AI Lab, Tencent, Shenzhen, China
| | - Chen Li
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | | | | | | | | | - Jamie Rossjohn
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
- Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Jiangning Song
- AI Lab, Tencent, Shenzhen, China
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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18
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Rappazzo CG, Fernández-Quintero ML, Mayer A, Wu NC, Greiff V, Guthmiller JJ. Defining and Studying B Cell Receptor and TCR Interactions. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:311-322. [PMID: 37459189 PMCID: PMC10495106 DOI: 10.4049/jimmunol.2300136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/15/2023] [Indexed: 07/20/2023]
Abstract
BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.
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Affiliation(s)
| | | | - Andreas Mayer
- Division of Infection and Immunity, University College London, London WC1E 6BT, UK
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
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19
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Yin R, Pierce BG. Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547832. [PMID: 37461571 PMCID: PMC10349958 DOI: 10.1101/2023.07.05.547832] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
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20
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Chu LS, Ruffolo JA, Harmalkar A, Gray JJ. Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.29.547134. [PMID: 37425754 PMCID: PMC10327054 DOI: 10.1101/2023.06.29.547134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi-track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. For a benchmark set of rigid targets, GeoDock obtains a 41% success rate, outperforming all the other tested methods. For a more challenging benchmark set of flexible targets, GeoDock achieves a similar number of top-model successes as the traditional method ClusPro [1], but fewer than ReplicaDock2 [2]. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey A Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
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21
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
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Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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22
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Yang YX, Huang JY, Wang P, Zhu BT. AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities. J Chem Inf Model 2023. [PMID: 37235532 DOI: 10.1021/acs.jcim.2c01499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Jin Yan Huang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
- Shenzhen Bay Laboratory, Shenzhen, 518055, China
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23
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Shanshal M, Caimi PF, Adjei AA, Ma WW. T-Cell Engagers in Solid Cancers-Current Landscape and Future Directions. Cancers (Basel) 2023; 15:2824. [PMID: 37345160 DOI: 10.3390/cancers15102824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/15/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023] Open
Abstract
Monoclonal antibody treatment initially heralded an era of molecularly targeted therapy in oncology and is now widely applied in modulating anti-cancer immunity by targeting programmed cell receptors (PD-1, PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and, more recently, lymphocyte-activation gene 3 (LAG3). Chimeric antigen receptor T-cell therapy (CAR-T) recently proved to be a valid approach to inducing anti-cancer immunity by directly modifying the host's immune cells. However, such cell-based therapy requires extensive resources such as leukapheresis, ex vivo modification and expansion of cytotoxic T-cells and current Good Manufacturing Practice (cGMP) laboratories and presents significant logistical challenges. Bi-/trispecific antibody technology is a novel pharmaceutical approach to facilitate the engagement of effector immune cells to potentially multiple cancer epitopes, e.g., the recently approved blinatumomab. This opens the opportunity to develop 'off-the-shelf' anti-cancer agents that achieve similar and/or complementary anti-cancer effects as those of modified immune cell therapy. The majority of bi-/trispecific antibodies target the tumor-associated antigens (TAA) located on the extracellular surface of cancer cells. The extracellular antigens represent just a small percentage of known TAAs and are often associated with higher toxicities because some of them are expressed on normal cells (off-target toxicity). In contrast, the targeting of intracellular TAAs such as mutant RAS and TP53 may lead to fewer off-target toxicities while still achieving the desired antitumor efficacy (on-target toxicity). Here, we provide a comprehensive review on the emerging field of bi-/tri-specific T-cell engagers and potential therapeutic opportunities.
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Affiliation(s)
| | | | | | - Wen Wee Ma
- Cleveland Clinic, Cleveland, OH 44195, USA
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24
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Wodak SJ, Vajda S, Lensink MF, Kozakov D, Bates PA. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annu Rev Biophys 2023; 52:183-206. [PMID: 36626764 PMCID: PMC10885158 DOI: 10.1146/annurev-biophys-102622-084607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
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Affiliation(s)
- Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium;
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA;
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Marc F Lensink
- Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France;
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA;
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom;
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25
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Moazzeni A, Kheirandish M, Khamisipour G, Rahbarizadeh F. Directed targeting of B-cell maturation antigen-specific CAR T cells by bioinformatic approaches: From in-silico to in-vitro. Immunobiology 2023; 228:152376. [PMID: 37058845 DOI: 10.1016/j.imbio.2023.152376] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/13/2023] [Accepted: 03/05/2023] [Indexed: 04/16/2023]
Abstract
AIMS Chimeric Antigen Receptor (CAR) T-cell is a breakthrough in cancer immunotherapy. The primary step of successful CAR T cell therapy is designing a specific single-chain fragment variable (scFv). This study aims to verify the designed anti-BCMA (B cell maturation antigen) CAR using bioinformatic techniques with the following experimental evaluations. MAIN METHODS Following the second generation of anti-BCMA CAR designing, the protein structure, function prediction, physicochemical complementarity at the ligand-receptor interface, and biding sites analysis of anti-BCMA CAR construct were confirmed using different modeling and docking server, including Expasy, I-TASSER, HDock, and PyMOL software. To generate CAR T-cells, isolated T cells were transduced. Then, anti-BCMA CAR mRNA and its surface expression were confirmed by real-time -PCR and flow cytometry methods, respectively. To evaluate the surface expression of anti-BCMA CAR, anti-(Fab')2 and anti-CD8 antibodies were employed. Finally, anti-BCMA CAR T cells were co-cultured with BCMA+/- cell lines to assess the expression of CD69 and CD107a as activation and cytotoxicity markers. KEY FINDINGS In-silico results approved the suitable protein folding, perfect orientation, and correct locating of functional domains at the receptor-ligand binding site. The in-vitro results confirmed high expression of scFv (89 ± 1.15% (and CD8α (54 ± 2.88%). The expression of CD69 (91.97 ± 1.7%) and CD107a (92.05 ± 1.29%) were significantly increased, indicating appropriate activation and cytotoxicity. SIGNIFICANCE In-silico studies before experimental assessments are crucial for state-of-art CAR designing. Highly activation and cytotoxicity of anti-BCMA CAR T-cell revealed that our CAR construct methodology would be applicable to define the road map of CAR T cell therapy.
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Affiliation(s)
- Ali Moazzeni
- Immunology Department, Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine (IBTO), Tehran, Iran
| | - Maryam Kheirandish
- Immunology Department, Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine (IBTO), Tehran, Iran.
| | - Gholamreza Khamisipour
- Department of Hematology, Faculty of Allied Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
| | - Fatemeh Rahbarizadeh
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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26
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Si Y, Yan C. Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models. Brief Bioinform 2023; 24:7033302. [PMID: 36759333 DOI: 10.1093/bib/bbad039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
The knowledge of contacting residue pairs between interacting proteins is very useful for the structural characterization of protein-protein interactions (PPIs). However, accurately identifying the tens of contacting ones from hundreds of thousands of inter-protein residue pairs is extremely challenging, and performances of the state-of-the-art inter-protein contact prediction methods are still quite limited. In this study, we developed a deep learning method for inter-protein contact prediction, which is referred to as DRN-1D2D_Inter. Specifically, we employed pretrained protein language models to generate structural information-enriched input features to residual networks formed by dimensional hybrid residual blocks to perform inter-protein contact prediction. Extensively bechmarking DRN-1D2D_Inter on multiple datasets, including both heteromeric PPIs and homomeric PPIs, we show DRN-1D2D_Inter consistently and significantly outperformed two state-of-the-art inter-protein contact prediction methods, including GLINTER and DeepHomo, although both the latter two methods leveraged the native structures of interacting proteins in the prediction, and DRN-1D2D_Inter made the prediction purely from sequences. We further show that applying the predicted contacts as constraints for protein-protein docking can significantly improve its performance for protein complex structure prediction.
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Affiliation(s)
- Yunda Si
- School of Physics, Huazhong University of Science and Technology, China
| | - Chengfei Yan
- School of Physics, Huazhong University of Science and Technology, China
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27
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Sabitova M, Beglov D, Vajda S, Kozakov D. Mapping of antibody epitopes based on docking and homology modeling. Proteins 2023; 91:171-182. [PMID: 36088633 PMCID: PMC9822860 DOI: 10.1002/prot.26420] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/25/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.
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Affiliation(s)
- Israel T. Desta
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | | | | | - Daron M. Standley
- Department of Genome Informatics, Osaka University, Osaka, 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, 565-0871, Japan
| | - Maria Sabitova
- Department of Mathematics, CUNY Queens College, Flushing, NY 11367, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
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28
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. CELL REPORTS METHODS 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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29
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Brookes JC, Gray ER, Loynachan CN, Gut MJ, Miller BS, P S Brogan A, McKendry RA. Thermodynamic analysis of an entropically driven, high-affinity nanobody-HIV p24 interaction. Biophys J 2023; 122:279-289. [PMID: 36527237 PMCID: PMC9892613 DOI: 10.1016/j.bpj.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 11/17/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions are fundamental to life processes. Complementary computational, structural, and biophysical studies of these interactions enable the forces behind their specificity and strength to be understood. Antibody fragments such as single-chain antibodies have the specificity and affinity of full antibodies but a fraction of their size, expediting whole molecule studies and distal effects without exceeding the computational capacity of modeling systems. We previously reported the crystal structure of a high-affinity nanobody 59H10 bound to HIV-1 capsid protein p24 and deduced key interactions using all-atom molecular dynamics simulations. We studied the properties of closely related medium (37E7) and low (48G11) affinity nanobodies, to understand how changes of three (37E7) or one (48G11) amino acids impacted these interactions; however, the contributions of enthalpy and entropy were not quantified. Here, we report the use of qualitative and quantitative experimental and in silico approaches to separate the contributions of enthalpy and entropy. We used complementary circular dichroism spectroscopy and molecular dynamics simulations to qualitatively delineate changes between nanobodies in isolation and complexed with p24. Using quantitative techniques such as isothermal titration calorimetry alongside WaterMap and Free Energy Perturbation protocols, we found the difference between high (59H10) and medium (37E7) affinity nanobodies on binding to HIV-1 p24 is entropically driven, accounted for by the release of unstable waters from the hydrophobic surface of 59H10. Our results provide an exemplar of the utility of parallel in vitro and in silico studies and highlight that differences in entropic interactions between amino acids and water molecules are sufficient to drive orders of magnitude differences in affinity.
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Affiliation(s)
- Jennifer C Brookes
- London Centre for Nanotechnology, Faculty of Maths and Physical Sciences, University College London, London, United Kingdom
| | - Eleanor R Gray
- London Centre for Nanotechnology, Faculty of Maths and Physical Sciences, University College London, London, United Kingdom
| | - Colleen N Loynachan
- Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Michelle J Gut
- London Centre for Nanotechnology, Faculty of Maths and Physical Sciences, University College London, London, United Kingdom
| | - Benjamin S Miller
- London Centre for Nanotechnology, Faculty of Maths and Physical Sciences, University College London, London, United Kingdom
| | - Alex P S Brogan
- Department of Chemistry, King's College London, London, United Kingdom
| | - Rachel A McKendry
- London Centre for Nanotechnology, Division of Medicine and Faculty of Maths and Physical Sciences, University College London, London, United Kingdom.
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30
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Fernández-Quintero ML, Kokot J, Waibl F, Fischer ALM, Quoika PK, Deane CM, Liedl KR. Challenges in antibody structure prediction. MAbs 2023; 15:2175319. [PMID: 36775843 PMCID: PMC9928471 DOI: 10.1080/19420862.2023.2175319] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023] Open
Abstract
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.
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Affiliation(s)
| | - Janik Kokot
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Franz Waibl
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Patrick K. Quoika
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics, Technical University of Munich, Garching, Germany
| | | | - Klaus R. Liedl
- CONTACT Klaus R. Liedl Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
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31
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Belapure J, Sorokina M, Kastritis PL. IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures. Protein Sci 2023; 32:e4523. [PMID: 36454539 PMCID: PMC9793972 DOI: 10.1002/pro.4523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/14/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022]
Abstract
Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.
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Affiliation(s)
- Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein CenterMartin Luther University Halle‐WittenbergHalle/SaaleGermany
| | - Marija Sorokina
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle‐WittenbergHalle/SaaleGermany,RGCC International GmbHZugSwitzerland,BioSolutions GmbHHalle/SaaleGermany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein CenterMartin Luther University Halle‐WittenbergHalle/SaaleGermany,Institute of Biochemistry and Biotechnology, Martin Luther University Halle‐WittenbergHalle/SaaleGermany,Biozentrum, Martin Luther University Halle‐WittenbergHalle/SaaleGermany
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32
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Yang YX, Wang P, Zhu BT. Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas. J Mol Graph Model 2023; 118:108364. [PMID: 36356467 DOI: 10.1016/j.jmgm.2022.108364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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33
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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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34
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Guo Z, Yamaguchi R. Machine learning methods for protein-protein binding affinity prediction in protein design. FRONTIERS IN BIOINFORMATICS 2022; 2:1065703. [PMID: 36591334 PMCID: PMC9800603 DOI: 10.3389/fbinf.2022.1065703] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.
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Affiliation(s)
- Zhongliang Guo
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan,Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan,*Correspondence: Rui Yamaguchi,
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35
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Meysman P. Simulations that capture antigen-antibody complexity. NATURE COMPUTATIONAL SCIENCE 2022; 2:781-782. [PMID: 38177396 DOI: 10.1038/s43588-022-00379-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Pieter Meysman
- ADREM data lab, University of Antwerp, Antwerp, Belgium.
- AUDACIS consortium, University of Antwerp, Antwerp, Belgium.
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36
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Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches. Vaccines (Basel) 2022; 10:vaccines10101729. [PMID: 36298594 PMCID: PMC9611379 DOI: 10.3390/vaccines10101729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022] Open
Abstract
Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host’s immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.
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Liang T, Jiang C, Yuan J, Othman Y, Xie XQ, Feng Z. Differential performance of RoseTTAFold in antibody modeling. Brief Bioinform 2022; 23:bbac152. [PMID: 35598325 PMCID: PMC9487640 DOI: 10.1093/bib/bbac152] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/28/2022] [Accepted: 04/06/2022] [Indexed: 12/31/2023] Open
Abstract
Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.
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Affiliation(s)
- Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Chen Jiang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jiayi Yuan
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yasmin Othman
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Cohen T, Halfon M, Schneidman-Duhovny D. NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning. Front Immunol 2022; 13:958584. [PMID: 36032123 PMCID: PMC9411858 DOI: 10.3389/fimmu.2022.958584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/15/2022] [Indexed: 11/20/2022] Open
Abstract
Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: https://github.com/dina-lab3D/NanoNet
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Affiliation(s)
- Tomer Cohen
- *Correspondence: Tomer Cohen, ; Dina Schneidman-Duhovny,
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39
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Yin R, Feng BY, Varshney A, Pierce BG. Benchmarking
AlphaFold
for protein complex modeling reveals accuracy determinants. Protein Sci 2022; 31:e4379. [PMID: 35900023 PMCID: PMC9278006 DOI: 10.1002/pro.4379] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 12/17/2022]
Abstract
High‐resolution experimental structural determination of protein–protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near‐native models (medium or high critical assessment of predicted interactions accuracy) generated as top‐ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein–protein docking (9% success rate for near‐native top‐ranked models), however AlphaFold modeling of antibody–antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer‐optimized version of AlphaFold (AlphaFold‐Multimer) with a set of recently released antibody–antigen structures confirmed a low rate of success for antibody–antigen complexes (11% success), and we found that T cell receptor–antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end‐to‐end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein–protein interaction of interest.
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Affiliation(s)
- Rui Yin
- Institute for Bioscience and Biotechnology Research University of Maryland Rockville Maryland USA
- Department of Cell Biology and Molecular Genetics University of Maryland College Park Maryland USA
| | - Brandon Y. Feng
- Department of Computer Science University of Maryland College Park Maryland USA
| | - Amitabh Varshney
- Department of Computer Science University of Maryland College Park Maryland USA
| | - Brian G. Pierce
- Institute for Bioscience and Biotechnology Research University of Maryland Rockville Maryland USA
- Department of Cell Biology and Molecular Genetics University of Maryland College Park Maryland USA
- Marlene and Stewart Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine Baltimore Maryland USA
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40
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Lee JH, Yin R, Ofek G, Pierce BG. Structural Features of Antibody-Peptide Recognition. Front Immunol 2022; 13:910367. [PMID: 35874680 PMCID: PMC9302003 DOI: 10.3389/fimmu.2022.910367] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.
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Affiliation(s)
- Jessica H. Lee
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
| | - Rui Yin
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States,University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Gilad Ofek
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States,University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Brian G. Pierce
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States,University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, United States,*Correspondence: Brian G. Pierce,
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41
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Tran MH, Schoeder CT, Schey KL, Meiler J. Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook. Front Immunol 2022; 13:859964. [PMID: 35720345 PMCID: PMC9204306 DOI: 10.3389/fimmu.2022.859964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022] Open
Abstract
Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.
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Affiliation(s)
- Minh H. Tran
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, United States
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Clara T. Schoeder
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
| | - Kevin L. Schey
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Jens Meiler
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
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42
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Hummer AM, Abanades B, Deane CM. Advances in computational structure-based antibody design. Curr Opin Struct Biol 2022; 74:102379. [PMID: 35490649 DOI: 10.1016/j.sbi.2022.102379] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022]
Abstract
Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.
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Affiliation(s)
- Alissa M Hummer
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@AlissaHummer
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@brennanaba
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
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43
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Why are large conformational changes well described by harmonic normal modes? Biophys J 2021; 120:5343-5354. [PMID: 34710378 DOI: 10.1016/j.bpj.2021.10.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 09/14/2021] [Accepted: 10/20/2021] [Indexed: 12/11/2022] Open
Abstract
Low-frequency normal modes generated by elastic network models tend to correlate strongly with large conformational changes of proteins, despite their reliance on the harmonic approximation, which is only valid in close proximity of the native structure. We consider 12 variants of the torsional network model (TNM), an elastic network model in torsion angle space, that adopt different sets of torsion angles as degrees of freedom and reproduce with similar quality the thermal fluctuations of proteins but present drastic differences in their agreement with conformational changes. We show that these differences are related to the extent of the deviations from the harmonic approximation, assessed through an anharmonic energy function whose harmonic approximation coincides with the TNM. Our results indicate that mode anharmonicity is more strongly related to its collectivity, i.e., the number of atoms displaced by the mode, than to its amplitude; low-frequency modes can remain harmonic even at large amplitudes, provided they are sufficiently collective. Finally, we assess the potential benefits of different strategies to minimize the impact of anharmonicity. The reduction of the number of degrees of freedom or their regularization by a torsional harmonic potential significantly improves the collectivity and harmonicity of normal modes and the agreement with conformational changes. In contrast, the correction of normal mode frequencies to partially account for anharmonicity does not yield substantial benefits. The TNM program is freely available at https://github.com/ugobas/tnm.
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44
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Paratope states in solution improve structure prediction and docking. Structure 2021; 30:430-440.e3. [PMID: 34838187 DOI: 10.1016/j.str.2021.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/10/2021] [Accepted: 11/03/2021] [Indexed: 12/13/2022]
Abstract
Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.
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45
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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Yin R, Guest JD, Taherzadeh G, Gowthaman R, Mittra I, Quackenbush J, Pierce BG. Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants. PLoS Comput Biol 2021; 17:e1009380. [PMID: 34491988 PMCID: PMC8448325 DOI: 10.1371/journal.pcbi.1009380] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/17/2021] [Accepted: 08/25/2021] [Indexed: 11/19/2022] Open
Abstract
The SARS-CoV-2 pandemic highlights the need for a detailed molecular understanding of protective antibody responses. This is underscored by the emergence and spread of SARS-CoV-2 variants, including Alpha (B.1.1.7) and Delta (B.1.617.2), some of which appear to be less effectively targeted by current monoclonal antibodies and vaccines. Here we report a high resolution and comprehensive map of antibody recognition of the SARS-CoV-2 spike receptor binding domain (RBD), which is the target of most neutralizing antibodies, using computational structural analysis. With a dataset of nonredundant experimentally determined antibody-RBD structures, we classified antibodies by RBD residue binding determinants using unsupervised clustering. We also identified the energetic and conservation features of epitope residues and assessed the capacity of viral variant mutations to disrupt antibody recognition, revealing sets of antibodies predicted to effectively target recently described viral variants. This detailed structure-based reference of antibody RBD recognition signatures can inform therapeutic and vaccine design strategies.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ipsa Mittra
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Jane Quackenbush
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
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van Noort CW, Honorato RV, Bonvin AMJJ. Information-driven modeling of biomolecular complexes. Curr Opin Struct Biol 2021; 70:70-77. [PMID: 34139639 DOI: 10.1016/j.sbi.2021.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/10/2021] [Indexed: 11/15/2022]
Abstract
Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.
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Affiliation(s)
- Charlotte W van Noort
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Rodrigo V Honorato
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands.
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Robustification of RosettaAntibody and Rosetta SnugDock. PLoS One 2021; 16:e0234282. [PMID: 33764990 PMCID: PMC7993800 DOI: 10.1371/journal.pone.0234282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.
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